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Switching to the TCU-based pricing model for Amazon Timestream queries can potentially reduce throttling, but it's not directly because of the pricing model itself. Rather, it's due to the increased control and scalability that comes with the TCU model.
With the TCU-based model, you can configure the maximum number of Timestream Compute Units (MaxQueryTCU) for your account. This allows you to allocate more compute resources to your queries, which can help reduce throttling by increasing the concurrent query capacity.
TCUs represent serverless compute capacity, comprising 4 vCPUs and 16 GB of memory per unit. By increasing your MaxQueryTCU, you're effectively increasing the available compute resources for your queries. This means more queries can be processed concurrently, reducing the likelihood of throttling during high-demand periods.
For example, in a simulated infrastructure monitoring use case, 4 TCUs were able to support 76 queries per second with low latency, while 8 TCUs supported 159 queries per second. This scalability allows you to handle higher query volumes more efficiently.
It's important to note that the pricing model itself doesn't directly impact throttling. Instead, it's the ability to allocate and scale compute resources (TCUs) that can help reduce throttling. The TCU model gives you more fine-grained control over your query resources, allowing you to balance performance needs with cost considerations.
However, keep in mind that while increasing MaxQueryTCU can help reduce throttling, it may also increase costs if those additional resources are utilized. You're billed for the TCUs your queries actually consume, so it's important to monitor usage and adjust the MaxQueryTCU setting as needed to optimize both performance and cost.
Sources
Understanding and optimizing Amazon Timestream Compute Units for efficient time series data management | AWS Database Blog
Announcing Timestream Compute Unit (TCU) for Amazon Timestream for LiveAnalytics
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